DEP-Former: Multimodal Depression Recognition Based on Facial Expressions and Audio Features via Emotional Changes

• Published in: IEEE transactions on circuits and systems for video technology Vol. 35; no. 3; pp. 2087 - 2100
• Main Authors: Ye, Jiayu, Yu, Yanhong, Lu, Lin, Wang, Hao, Zheng, Yunshao, Liu, Yang, Wang, Qingxiang
• Format: Journal Article
• Language: English
• Published: IEEE 01.03.2025
• Subjects: audio features; Circuits and systems; Data collection; Data models; Deep learning; DEP-Former; Depression; depression recognition; Emotion recognition; EWRE; Face recognition; facial expressions; Feature extraction; Indexes; Mental health
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2024.3491098

Clinical research has demonstrated that exploring behavioral signal differences between depressed patients and non-depressed people using audiovisual technology is an effective approach for achieving depression recognition. Hence, in this paper we propose an emotion word reading experiment (EWRE), and extract features from facial expressions and audios for depression recognition. Building upon this, we propose a depression recognition model (DEP-Former), which deeply integrates multimodal features. DEP-Former first designs a modality adapter to achieve emotion space mapping and the sharing of multimodal features, addressing cross-modal inconsistencies. Simultaneously, it proposes a mechanism of attention index sharing, exceeding the limitations of cognitive subjectivity by calculating confidence in key emotional information across modalities. Finally, we propose a multimodal cross-attention module and a Bernoulli distribution feature fusion prediction module to achieve deep integration of multilevel information, thereby enabling depression recognition. Compared with existing advanced multimodal models, DEP-Former demonstrates superior performance in EWRE, achieving an accuracy of 0.9500 and an F1 score of 0.9499, significantly enhancing depression recognition over the single-modality methods. Furthermore, its robust generalization ability is validated on the AVEC 2014 dataset. Through the attention query of the interpretability analysis module, we discover that depressed patients exhibit heightened sensitivity to negative emotional words, such as dismissal and tragedy. In contrast, healthy individuals tend to be more attuned to positive emotional words, including passion, purity, and justice. Additionally, depressed patients exhibit a degree of psychological state diversity, showing sensitivity to some positive emotional words as well. Our codes and data are available at https://github.com/QLUTEmoTechCrew/DEP-Former .